Video Super-Resolution Using Reference Frames and Scene-Cut Detection
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Solution Overview
Problem
Existing video super-resolution methods fail to effectively learn the inverse representation of screen content videos, which have sharp edges, high contrast textures, and noiseless contents, and do not consider scene-cuts or local mutations, leading to suboptimal performance in natural scene videos and being hindered by compression distortion.
Innovation Solution
The proposed method involves using reference frames, including neighboring forward and backward frames, to determine differences and generate higher resolution images by concatenating distance maps with feature maps, and employing a multi-stage SR solution with adaptive training schemes to refine outputs.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Manufacturing precision
If conventional super-resolution methods are used, then processing speed is maintained, but image quality deteriorates due to failure in learning inverse representation of screen content videos and not considering scene-cuts or local mutations
Solution Approach 1:
The video is divided into multiple frames, and the method segments the super-resolution process by identifying scene-cuts and local mutations to apply different processing strategies to different segments. This allows the system to handle natural scene videos and screen content videos differently, improving overall image quality without uniformly increasing complexity across all video content.
Solution Approach 2:
The patent applies local quality by detecting scene-cuts and local mutations to identify regions requiring different super-resolution treatments. Natural scene regions receive one type of processing while screen content regions receive another, allowing each local area to be processed with the most appropriate method for its specific characteristics, thereby improving image quality without uniformly complicating the entire processing pipeline.
2Manufacturing precision
If multi-stage SR solution with adaptive training schemes is employed, then super-resolution performance improves for natural scene and screen content videos, but computational complexity increases
Solution Approach 1:
The patent employs dynamic adaptive training schemes that adjust the super-resolution process based on detected scene-cuts and local mutations. The system dynamically switches between different processing stages and parameters depending on the video content type (natural scene vs. screen content), improving resolution performance while avoiding unnecessary computational energy expenditure on uniform video segments that require less intensive processing.
3Measurement precision
If reference frames including neighboring forward and backward frames are used, then accuracy improves by accounting for compression distortions, but processing time increases
Solution Approach 1:
The patent performs preliminary action by pre-identifying scene-cuts and local mutations in the video sequence before applying super-resolution. By detecting these critical points in advance and preparing appropriate processing strategies, the system can quickly switch between different reference frame usage strategies, improving accuracy through proper handling of compression distortions while minimizing processing time through advance preparation.
Data Source
AI summary
Embodiments of the present disclosure provide a solution for image/video super resolution. A method for image processing is proposed. The method comprises: receiving a first image with a first resolution and at least one reference image associated with the first image, the first image and the at least one reference image being associated with a same video; determining a difference between the first image and the at least one reference image; and generating a second image with a second resolution based on the difference, the first image and the at least one reference image, the second resolution being higher than the first resolution.